无需训练，灵活注入人物形象，让生成图像既保真又贴合文字描述。
FlexID: Training-Free Flexible Identity Injection via Intent-Aware Modulation for Text-to-Image Generation
- 通过语义投影与视觉锚点分离身份信息，实现双路调控。
- 动态调节权重，根据编辑意图自动放松视觉约束，提升文本适配性。
- 适合需要频繁更换角色或复杂叙事的生成场景，效率高且效果佳。
个性化文本到图像生成旨在将特定身份无缝融入文本描述中。然而，现有无需训练的方法通常依赖固定的视觉特征注入，导致身份保真度与文本适应性之间存在矛盾。为此，我们提出 FlexID，一种基于意图感知调制的新型无需训练框架。FlexID 将身份信息正交解耦为两个维度：语义身份投影器（SIP）将高层先验注入语言空间，视觉特征锚点（VFA）则在潜在空间中确保结构保真。关键在于引入上下文感知自适应门控（CAG）机制，根据编辑意图和扩散时间步动态调节两路信号的权重。当检测到强烈编辑意图时，自动放宽刚性视觉约束，实现身份保留与语义变化的协同。在 IBench 上的大量实验表明，FlexID 在身份一致性与文本遵循性之间达到当前最优平衡，为复杂叙事生成提供高效解决方案。
原文摘要 · Abstract (English)
Personalized text-to-image generation aims to seamlessly integrate specific identities into textual descriptions. However, existing training-free methods often rely on rigid visual feature injection, creating a conflict between identity fidelity and textual adaptability. To address this, we propose FlexID, a novel training-free framework utilizing intent-aware modulation. FlexID orthogonally decouples identity into two dimensions: a Semantic Identity Projector (SIP) that injects high-level priors into the language space, and a Visual Feature Anchor (VFA) that ensures structural fidelity within the latent space. Crucially, we introduce a Context-Aware Adaptive Gating (CAG) mechanism that dynamically modulates the weights of these streams based on editing intent and diffusion timesteps. By automatically relaxing rigid visual constraints when strong editing intent is detected, CAG achieves synergy between identity preservation and semantic variation. Extensive experiments on IBench demonstrate that FlexID achieves a state-of-the-art balance between identity consistency and text adherence, offering an efficient solution for complex narrative generation.
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